Linking in situ LAI and fine resolution remote sensing data to map reference LAI over cropland and grassland using geostatistical regression method

作者:He, YQ (He, Yaqian)[ 1,2,3 ] ; Bo, YC (Bo, Yanchen)[ 1,2 ] ; Chai, LL (Chai, Leilei)[ 1,2 ] ; Liu, XL (Liu, Xiaolong)[ 4 ] ; Li, AH (Li, Aihua)[ 5 ]

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

卷: 50  页: 26-38

DOI: 10.1016/j.jag.2016.02.010

出版年: AUG 2016

摘要

Leaf Area Index (LAI) is an important parameter of vegetation structure. A number of moderate resolution LAI products have been produced in urgent need of large scale vegetation monitoring. High resolution LAI reference maps are necessary to validate these LAI products. This, study used a geostatistical regression (GR) method to estimate LAI reference maps by linking in situ LAI and Landsat TM/ETM+ and SPOT-HRV data over two cropland and two grassland sites. To explore the discrepancies of employing different vegetation indices (VIs) on estimating LAI reference maps, this study established the GR models for different Vis, including difference vegetation index (DVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI). To further assess the performance of the GR model, the results from the GR and Reduced Major Axis (RMA) models were compared. The results show that the performance of the GR model varies between the cropland and grassland sites. At the cropland sites, the GR model based on DVI provides the best estimation, while at the grassland sites, the GR model based on DVI performs poorly. Compared to the RMA model, the GR model improves the accuracy of reference LAI maps in terms of root mean square errors (RMSE) and bias. (C) 2016 Elsevier B.V. All rights reserved.

通讯作者地址: Bo, YC (通讯作者)

Beijing Normal Univ, Sch Geog, Res Ctr Remote Sensing & GIS, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Sch Geog, Res Ctr Remote Sensing & GIS, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Beijing Key Lab Remote Sensing Environm & Digital, Beijing 100875, Peoples R China

[ 3 ] W Virginia Univ, Dept Geol & Geog, Morgantown, WV 26506 USA

[ 4 ] Yunnan Normal Univ, Coll Tourism & Geog Sci, Kunming 650500, Yunnan Province, Peoples R China

[ 5 ] Boise State Univ, Dept Geosci, Boise, ID 83725 USA